Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models

Tree species recognition accuracy greatly affects forest remote sensing mapping and forestry resource monitoring. The multispectral and texture features of the remote sensing images from the ZiYuan-3 (ZY-3) satellite at two phenological phases of autumn and winter (September 29th and December 7th) were selected for constructing and optimizing sensitive spectral indices and texture indices. Multidimensional cloud model and support vector machine (SVM) model were constructed by the screened spectral and texture indices for remote sensing recognition of Quercus acutissima (Q. acutissima) and Robinia pseudoacacia (R. pseudoacacia) on Mount Tai. The results showed that, the correlation intensities of the constructed spectral indices with tree species were preferable in winter than in autumn. The spectral indices constructed by band 4 showed the superior correlation compared with other bands, both in the autumn and winter time phases. The optimal sensitive texture indices for both phases were mean, homogeneity and contrast for Q. acutissima, and contrast, dissimilarity and second moment for R. pseudoacacia. Spectral features were found to have a higher recognition accuracy than textural features for recognizing on both Q. acutissima and R. pseudoacacia, and winter showing superior recognition accuracy than autumn, especially for Q. acutissima. The recognition accuracy of the multidimensional cloud model (89.98%) does not show a superior advantage over the one-dimensional cloud model (90.57%). The highest recognition accuracy derived from a three-dimensional SVM was 84.86%, which was lower than the cloud model (89.98%) in the same dimension. This study is expected to provide technical support for the precise recognition and forestry management on Mount Tai.

December 7 (Fig. 1c), 2014, were selected to recognize the tree species, considering the phenological characteristics of tree species, the satellite return cycle and weather factors. Mount Tai images from ZY-3 in 2014 include four scenes on June 13, September 29, October 9 and December 7. The vegetation was flourishing, with a criss-cross pattern, and easy to misclassify in June. Q. acutissima and R. pseudoacacia are changing leaf colour in September, and the shape and colour of the leaves were different and easier to identify. The beginning of December is the defoliating period of the deciduous broad-leaf forest, which is easily distinguished from the coniferous forest. The differences in the fresh leaf and canopy characteristics between the two tree species were larger and easier to identify than those in other phases. Moreover, by taking into account the image quality and closeness of the September 29 and October 9 dates, images from ZY-3 on September 29 (Fig. 1b) and December 7 (Fig. 1c), 2014, were finally selected. The images have 4 bands, with spectral ranges of 0.45-0.52 µm, 0.52-0.59 µm, 0.63-0.69 µm and 0.77-0.89 µm, and the spatial resolution is 5.8 m. Sample area selection in the plant species survey followed the principle of uniformity and typicality of tree species distribution. For example, the tree sampling plots were uniformly distributed in three elevation zones of 400-600 m, 600-800 m, and 800-1000 m. Based on the elevational distribution features of Q. acutissima and R. pseudoacacia in Mount Tai, the proportion of sample numbers in these three elevation zones followed the formula of 1:3:2. Each tree species sample plot are far from cloud cover and slope tops, large stream gullies and rocky outcrops, as shown in Fig. 1a. In addition, pixel differences could not be discounted and have been selected as experimental units. There were 2550 pixels in the Q. acutissima area and 1635 pixels in the R. pseudoacacia area. Equidistant sampling was applied to select 2/3 pixels as modelling samples (1700 of Q. acutissima and 1104 of R. pseudoacacia) and 1/3 pixels as validation samples (850 of Q. acutissima and 552 of R. pseudoacacia) for each tree species.
Research methods. The pre-processing for ZY-3 multispectral remote-sensing images, such as atmospheric correction, geometrical correction and radiometric correction, had been performed before the species recognition. Topographic radiometric correction was specifically verified multiple times to reduce the influence of the complex topography of Mount Tai on the recognition results. The model construction and tree identification technology flowchart are shown in Fig. 2.
Spectral indices. Mathematical algorithms were applied to construct the spectral indices based on the pixel reflectance of the multispectral images, the categories of 22 spectral indices and the corresponding mathematical formulations are shown as Table 1. A total of 166 spectral indices were constructed at each image phase (2014 September 29 and December 7) based on the four bands 35 . The logistic regression model was applied to the correlation analysis between the spectral indices and tree species, in which the spectral indices and tree species types were quantitative independent variables and qualitative dependent variables, respectively. The spectral indices with the highest correlation coefficients were determined to be sensitive spectral indices, which is used as the conditional attributes to construct one-dimensional or multi-dimensional cloud models and SVM models. Then, the three optimal sensitive spectral index was selected according to the recognition accuracy of the onedimensional model to construct the three-dimensional models.
Texture feature parameters. The grey level co-occurrence matrix is an algorithm proposed by Haralick et al. 36 to describe the texture features. This matrix is used to reflect the grey relation of pixels in a certain area and the distance in terms of direction, adjacent spacing, and amplitude of variation, which represents the spatial Winter image on December 7, 2014. The map was generated using "Esri ArcMap (10.6.0.8321)" package (https:// www. esri. com/ en-us/ arcgis/ produ cts/ arcgis-deskt op/ overv iew).  36 . Eight sensitive texture parameters were selected to extract texture information from ZY-3 remote sensing images: mean, variance, uniformity, contrast, dissimilarity, entropy, second moment and correlation. The texture features change with the size, direction and step of the window. The most important issue for efficiently acquiring texture information is to use an appropriate computation window, because texture information will be lost in a too small window, while it will face excessive computation and storage pressure in a too large window. In this study, different windows (3*3, 5*5, and 7*7) are exploited to extract texture information, and the best computation window will be finally selected for the concluding tree species recognition 37 .
Cloud model. The cloud model was proposed by Li Deyi in 1995. The probability density function is applied to the cloud model to capture the uncertainty in the membership degree, which features fuzziness and randomness. Cloud model reflect the quantitative features of qualitative concepts by the digital characteristics of expect (Ex), entropy (En), and hyper-Entropy (He). Ex represents the most typical sample of a qualitative conceptual quantification. En reflects the uncertainty of the qualitative concepts; the greater En is, the more macroscopic the concept, and the greater the fuzziness and randomness are, the more difficult the concept quantification. He reflects the uncertainty of the entropy 19 . In this study, Ex denotes the typical eigenvalues of each tree species. En denotes the uncertainty of a tree species, which is the dispersion of the remote sensing feature information that could be classified in the sample area. He denotes the uncertainty of En, and it reflects the cohesion, i.e., cloud thickness, of the pixel belonging to a certain tree species. The cloud generator includes the forward and backward cloud generators, and the forward one is also named X conditional cloud generator, which is adaptable to the condition of the digital features Ex, En, He and the test sample are available. There are three steps in the tree species classification algorithm based on the cloud model. First, the cloud model of each tree species is generated by backward cloud generator. Second, the membership degree of the samples is calculated with the X conditional cloud generator. Finally, the tree species were recognized according to the maximum determination method 19 . In this study, a three-dimensional
(1) Backward cloud generator The sensitive indices or texture parameters of the modelling samples that represented the characteristic information of the tree species were input ( x i (i = 1, 2...., n) ); then, the tree characteristic values of the cloud model were output (Ex, En and He), The one-dimensional and three-dimensional backward cloud generators are shown in Fig. 3a,b, respectively.
(2) X conditional cloud generator The tree characteristic values of the cloud model (Ex, En, and He) and the corresponding sensitive indices or texture parameters of validation samples ( x 0 ) were input; then, the membership value for each tree species in each sample was output ( According to the maximum determination method, the membership values of the samples were calculated for every tree species 39 , and the tree species was recognized by selecting the maximum value. The principles of the one-dimensional and three-dimensional X conditional cloud generators and the maximum determination method are shown in Fig. 4a,b, respectively, in which u and v represent the tree species. Support vector machine. Support vector machine (SVM) is a pattern recognition method based on statistical learning theory 40 . The basic idea is to map the data from the original feature space to a high-dimensional feature space through the kernel function. Then, the optimal hyperplane in the feature space is established to maximize the classification interval, and the unknown samples can be recognized on the hyperplane 41 . Currently, the commonly used kernel functions are the linear kernel function, polynomial kernel function, radial basis function (RBF) and sigmoid kernel function. Studies have shown that SVM classifiers constructed with radial kernel functions have better classification results 9,42 . A radial kernel function was chosen to construct the SVM classifier in this study.

Results
Sensitive spectral index. The correlation coefficients between the spectral indices and tree species were analysed, and 10 spectral indices with the highest correlation values at each phase were selected as sensitive spectral indices, as shown in Table 2. The correlations coefficients between the spectral index and tree species were divided into eight categories, i.e., 0.00 to ± 0.30, ± 0.30 to ± 0.50, ± 0.50 to ± 0.80, and ± 0.80 to ± 1.00, which indicating micro, real, significant and highly positive or negative correlations 43,44 , as shown in Fig. 5.
The spectral indices X 1 − X 7 on September 29 shown the real positively and negatively correlation with tree species, and X 8 − X 10 on September 29 shown significant positive or negative correlation. The spectral indices X 1 − X 10 on December 7 all shown significant positive and negative correlation with tree species. The correlation on December 7 was generally higher than that on September 29. The most dominant bands included in all sensitive spectral indices are band 4 on September 29 and band 3 and 4 on December 7, which indicates that the spectral indices constructed by band 3 and 4 shown the most robust correlation with the tree species.
One-dimensional cloud model. Spectral features. The sensitive spectral indices of both Q. acutissima and R. pseudoacacia were used for obtaining the respective indices eigenvalues to construct the one-dimensional cloud models, as shown in Fig. 6a,b. The cloud model eigenvalues, expect (Ex), entropy (En), and hyper-Entropy  www.nature.com/scientificreports/ (He), showed significant differences with sensitive spectral indices for different tree species. The shapes of the cloud droplet combinations for the cloud models of Q. acutissima and R. pseudoacacia are shown in Fig. 6a,b. The recognition accuracy of the one-dimensional cloud model between different tree species has obvious variability, and the cloud models constructed with different sensitive spectral indices for one tree species also showed variable performance, as shown in Fig. 6c,d. On September 29, X 5 had the highest accuracy (87.29%) for recognizing Q. acutissima, followed by X 7 (85.41%) and X 8 (83.65%), and the rest sensitive spectral indices were all higher than 69.18%. For R. pseudoacacia, X 9 had the highest accuracy (71.02%), followed by X 10 (68.66%) and X 1 (67.57%), and the other sensitive spectral indices were all higher than 55.98%. On December 7, X 10 had the highest accuracy (91.65%) for the recognition of Q. acutissima, followed by X 5 (90.47%) and X 3 (90.12%), and the other sensitive spectral indices were all higher than 82.82%. For R. pseudoacacia, X 6 and X 7 had the highest accuracies (89.49%), followed by X 9 (89.31%), and the other sensitive spectral indices were all higher than 78.32%.
Generally, the average recognition accuracies on Q. acutissima and R. pseudoacacia were 78.46% and 63.50% on September 29, and 87.15% and 83.68% on December 7. We found that the recognition performance of the one-dimensional cloud model constructed by the sensitive spectral index of Q. acutissima was superior to that of R. pseudoacacia. Meanwhile, the recognition accuracy of December 7 spectral data derived from ZY-3 images was significantly higher than that of September 29 for both Q. acutissima and R. pseudoacacia.
Texture features. The recognition results on tree species based on texture features varied under different windows. Q. acutissima had the best recognition accuracy under 3*3 window, and R. pseudoacacia had the highest recognition accuracy under 5*5 window. Therefore, the optimal recognition accuracy of Q. acutissima under 3*3 window and that of R. pseudoacacia under 5*5 window were selected for the final accuracy analysis. The texture feature parameters Y 1 , Y 3 , and Y 4 shown the highest recognition accuracy of 82.31%, 82.59% and 76.92% for Q. acutissima on September 29, while 85.71%, 83.56% and 79.82% on December 7, as shown in Fig. 7a. The texture feature parameters Y 5 , Y 7 , and Y 4 shown the highest recognition accuracy of 65.52%, 62.07% and 58.62% for R. pseudoacacia on September 29, while 71.55%, 69.82% and 65.49% on December 7, as shown in Fig. 7b. The recognition accuracy on December 7 was higher than that on September 29. In which Y 1 had the Table 2. Sensitive spectral indices. X 1 − X 10 represents the categories of the 10 sensitive spectral indices. R represents the different band reflectance of the images.

September 29
December 7  Three-dimensional cloud model. The three-dimensional cloud models were constructed by the optimal three sensitive spectral indices or the three optimal texture feature parameters selected by the recognition accuracy of the one-dimensional cloud model. The optimal sensitive spectral indices on September 29 were X 5 , X 7 and X 8 for Q. acutissima and X 1 , X 9 and X 10 for R. pseudoacacia. The optimal sensitive spectral indices on December 7 were X 3 , X 5 and X 10 for Q. acutissima and X 6 , X 7 and X 9 for R. pseudoacacia. The optimal texture feature parameters of the two temporal phases were Y 1 (mean), Y 3 (homogeneity) and Y 4 (contrast) for Q. acutissima and Y 4 (contrast), Y 5 (dissimilarity) and Y 7 (second moment) for R. pseudoacacia, as shown in Fig. 5. Figure 8 shows that the overall recognition accuracies for the two tree species on September 29 and December 7 were 78.25% and 89.89% based on the spectral features and 73.75% and 78.92% based on the texture features, respectively. In summary, the remote sensing recognition accuracies of the three-dimensional cloud models for Q. acutissima and R. pseudoacacia showed that December 7 was superior to September 29 and the spectral features were superior to the texture features. Moreover, the recognition accuracy of the three-dimensional cloud model on Q. acutissima was generally higher than that of R. pseudoacacia, and the three-dimensional cloud model was not found to have significantly improved performance over the one-dimensional cloud model. Support vector machine. The spectral features on December 7 showed a high accuracy and used to construct the SVM recognition models as contrast with cloud model, in which the one-dimensional and threedimensional SVM (SVM 1 and SVM 3 ) were constructed separately by 10 single sensitive spectral indices and 3  www.nature.com/scientificreports/ optimal sensitive spectral indices. Modelling, accuracy verification and parameter optimization were performed to determine that C-support vector classification (C-SVC) as the SVM type and RBF as the SVM kernel function.
In addition, the SVM model parameters constructed by different spectral index variables were kept consistent to ensure the SVM recognition results were relatively comparable, as shown in Table 3.
The recognition accuracies of the cloud models and SVM are shown in Fig. 9. SVM 1 and SVM 3 had recognition accuracies of 85.45% and 85.94% for Q. acutissima and 83.51% and 83.78% for R. pseudoacacia, respectively, and non significant differences in the recognition results were found between SVM 3 and SVM 1 . The recognition performance of the cloud model and SVM was compared and found that the optimal recognition accuracy of SVM 3 for the two tree species is 84.86%, which is lower than the 89.98% from three-dimensional cloud model. Meanwhile, the optimal recognition accuracy of SVM 1 for the two tree species is 84.48%, which is lower than the 90.57% of the one-dimensional cloud model. The average recognition accuracy of 77.72% from 10 SVM 1 models constructed by the 10 sensitive spectral indices was also lower than that of 85.42% from 10 onedimensional cloud models. The results indicate that the cloud model outperforms the support vector machine for the recognition of Q. acutissima and R. pseudoacacia.

Discussion
Spectral and texture features from ZiYuan-3 satellite were put into one-dimensional or multi-dimensional cloud model and support vector machine model for the remote sensing recognition of Q. acutissima and R. pseudoacacia on Mount Tai. The recognition accuracies of the tree species were analysed to find the discriminative image features and optimal recognition model in this study.
Tree species recognition performance of the cloud model constructed with both sensitive spectral indices and texture feature parameters was found to be superior on December 7 than on September 29. The explanation for the difference in recognition performance is speculated to be, on the one hand, the rough discrimination of the canopy layer due to the luxurious growth of the broadleaf species. On the other hand, the leaf color of deciduous species was undergoing a regular change in late autumn 45 , and the mixture of green and yellow leaf colors contributed to the confusion of the spectral features from specific tree species. In contrast, on December 7, Q. acutissima and R. pseudoacacia are experiencing the defoliation stage in early winter, when the leaves were fully discolored and partially defoliated. Then the trunk morphological differences exposed in this period provide characteristic spectral information, and the differences in leaf surface morphology, moisture and chlorophyll content of the tree species at different defoliation stages all contribute to the variations in spectral information.
The recognition accuracy by multispectral information on Q. acutissima was generally higher than that on R. pseudoacacia, which may be explained by the differences in crown width, tree height and leaf tyle. Moreover, altitude, gradient, slope direction, and tree age may also induce differences in the spectral reflectance and texture features of tree species. Accordingly, the investigation of the morphology, planting environment, and age of tree  Figure 9. Recognition accuracy comparison between the cloud model and SVM. Note: Q and R represent Q. acutissima and R. pseudoacacia, respectively. One-dimension and three-dimension represent the different dimensional recognition model. www.nature.com/scientificreports/ species is regarded as one of the critical issues for further development of forest survey and monitoring with the remote sensing technology. The performance improvement of remote sensing recognition in winter over autumn was more significant for Q. acutissima than for R. pseudoacacia. For example, the recognition accuracy of the cloud model constructed with five sensitive spectral indices X 4 − X 8 from the September 29 image was only about 60% for R. pseudoacacia (Fig. 6c), while the recognition accuracy from the December 7 image improved up to 90% (Fig. 6d). This indicates that the recognition accuracy of December 7 is improved by about 30% over that of September 29 for R. pseudoacacia, while Q. acutissima was found only improved by about 10%. The recognition performance of the cloud model constructed by the sensitive spectral indices from September 29 for Q. acutissima were all superior to that of R. pseudoacacia, while the recognition accuracy gap between the two species from December 7 was narrowed considerably, and even R. pseudoacacia surpassed Q. acutissima. Consequently, the recognition performance of multispectral information of R. pseudoacaci in winter was revealed to be significantly improved than that in autumn, and the explanation for this phenomenon was explored in this study. The average tree height is 6.93 m and the average diameter at breast height is 12.9 cm, according to Hao et al. 45 , who surveyed 238 Q. acutissima individuals in the field on Mount Tai. Mi et al. 46 concluded that the average tree height of Q. acutissima on Mount Tai is 8 m, diameter at breast height is 9.1 cm, and crown density is 0.5, while the average tree height of R. pseudoacaci is 8-9 m, diameter at breast height is 15 cm, and crown density is 0.8-0.9. Therefore, as the trunk is exposed after the leaves fall in winter, the larger size and canopy density as well as the curved trunk morphology of R. pseudoacaci make it more easily captured by remote sensing technology, and thus the recognition accuracy for R. pseudoacaci in winter was significantly improved.
Spectral features were found to have a higher recognition accuracy than textural features for recognizing on Q. acutissima and R. pseudoacacia. A potential explanation for this conclusion could be that the tree species features revealed by the spectral indices are a collection of multiple features such as canopy water content and leaf chlorophyll content, while the texture feature indices only mainly revealed information on the orthometric geometry of the tree species.
Researchers have evaluated the performance of the existing tree recognition algorithms for recognizing tree species. The minimum distance and maximum likelihood methods were the traditional algorithms in remote sensing recognition. These methods characterised by mature application, simple implementation, a small amount of computation and fast speeds, but the disadvantage is that the recognition accuracy was insufficient, and only the samples with obvious features could be correctly recognized. SVM recently have been widely used for the accurate recognition of forest species and shown higher recognition performance than other methods 12 . Clouds are a new and readily visualizable concept of uncertainty. Cloud models are an effective tool for converting qualitative concepts into quantitative formulations and are capable of capturing fuzzy and stochastic uncertainty with simple numerical features. Cloud model have the advantage of simplicity of operation and not needing extensive model computation. In this research, the cloud model has also been proved outperforming the support vector machine for the recognition on Q. acutissima and R. pseudoacacia. The recognition accuracy of the multidimensional model does not show a superior advantage over the one-dimensional model, both for cloud model and support vector machine. The one-dimensional cloud model was simple and required fewer spectral indices and calculation procedures, which can be used as the priority cloud model. The operation simplicity and performance superiority of cloud models will bring an extensive prospect for its application in remote sensing recognition on tree species.
Sample data availability of tree species is essential no matter which classification algorithm is applied. Both the quantity and quality of sample data should be greatly expanded in the future research on the tree species recognition of Mount Tai with remote sensing technology, which is the foundation for better recognition accuracy. The tree species involved in this study are relatively limited and should be widely expanded in future studies, providing a comprehensive technical support for forest resources survey and monitoring of Mount Tai.

Conclusions
The multispectral and texture features of the remote sensing images from the ZiYuan-3 (ZY-3) satellite at two phenological phases of autumn and winter (September 29th and December 7th) were selected for constructing and optimizing sensitive spectral indices and texture indices. Multidimensional cloud model and support vector machine model were constructed by the screened spectral and texture indices for remote sensing recognition of Q. acutissima and R. pseudoacacia on Mount Tai. The results showed that, the correlation intensities of the constructed spectral indices with tree species were preferable in winter than in autumn. The spectral indices constructed by band 4 showed the superior correlation compared with other bands, both in the autumn and winter time phases. The optimal sensitive texture parameters for both time phases were mean, homogeneity and contrast for Q. acutissima and contrast, dissimilarity and second moment for R. pseudoacacia. The texture parameters Y 1 (mean) have the highest recognition accuracy of 85.71% for Q. acutissima and Y 5 (dissimilarity) have the highest recognition accuracy of 71.55% for R. pseudoacacia. The recognition performance of the both one-dimensional and three-dimensional cloud model constructed by the sensitive spectral index of Q. acutissima was superior to that of R. pseudoacacia. Meanwhile, the recognition accuracy of December 7 spectral data derived from ZY-3 images was significantly higher than that of September 29 for both Q. acutissima and R. pseudoacacia. Spectral features were found to have a higher recognition accuracy than textural features for recognizing on Q. acutissima and R. pseudoacacia, and winter showing superior recognition accuracy than autumn, especially for Q. acutissima. The recognition accuracy of the multidimensional cloud model (89.98%) does not show a superior advantage over the one-dimensional cloud model (90.57%). The one-dimensional cloud model was simple and required fewer calculations, which can be used as the priority cloud model. Non significant differences in the recognition results were found between one-dimensional and three-dimensional support vector machine model. www.nature.com/scientificreports/ The highest recognition accuracy derived from SVM 3 was 84.86%, which was lower than the cloud model (89.98%) in the same dimension. The cloud model outperforms the support vector machine for the recognition of Q. acutissima and R. pseudoacacia. The research results are expected to provide technical support for the precise recognition and forestry management on Mount Tai.

Data availability
The datasets generated and/or analysed during the current study are not publicly available due the confidentiality agreements but are available from the corresponding author on reasonable request.